Powering Up With Space-Time Wind Forecasting

• Published in: Journal of the American Statistical Association Vol. 105; no. 489; pp. 92 - 104
• Main Authors: Hering, Amanda S., Genton, Marc G.
• Format: Journal Article
• Language: English
• Published: Alexandria American Statistical Association 01.03.2010; Assoc; Taylor & Francis Ltd
• Subjects: Applications and Case Studies; Electric power plants; Electricity; Forecasting models; Forecasting techniques; Modeling; Power generation; Power supply; Predictive modeling; Spacetime; Spacetime forecasting; Statistical analysis; Statistics; Time series models; Weather forecasting; Wind; Wind direction; Wind energy; Wind power; Wind velocity
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/jasa.2009.ap08117

The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, that is, highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an offsite location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting wind at other locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each model's predictions.